NeurIPS 2019 Competition Track

NeurIPS 2019 Accepted competitions

Below you will find a brief summary of competitions @ NeurIPS2019. Regular competitions take place before the NeurIPS, whereas live competitions will have their final phase during the competition session @NeurIPS2019. All prizes are tentative and depend solely on the organizing team of each competition and the corresponding sponsors.

Please note that all information is subject to change, please contact the organizers of each competition directly for more information.

A causal understanding of climatic interactions is of high societal relevance from identifying causes of extreme events to process understanding and weather forecasting. This competition comprises a number of multivariate time series datasets featuring major challenges of climate data from time delays and nonlinearity to nonstationarity and selection bias. The competition aims to open up new interdisciplinary research pathways by improving our scientific understanding of Earth’s climate, while also driving method development and benchmarking in the computer science community.

Build the best AI bot to play reconnaissance blind chess, a challenge for making optimal decisions in the face of uncertainty. Reconnaissance blind chess is like chess except a player does not know where her opponent's pieces are a priori. Rather, she can covertly sense a chosen 3x3 square of the board each turn and also learn partial information from captures.

The AutoDL challenge aims taking the automate the design of deep learning (DL) methods to solve generic tasks. This is a challenge with “code submission”: machine learning algorithms are trained and tested on a challenge platform on data invisible to the participants. We target applications such as speech, image, video, and text, for which DL methods have had great success recently, to drive the community to work on automating the design of DL models. Raw data will be provided, formatted in a uniform tensor manner, to encourage participants to submit generic algorithms. We will impose restrictions on training time and resources to push the state-of-the-art further. We will provide a large number of pre-formatted public datasets and set up a repository of data exchange to enable meta-learning.

Autonomous cars are expected to dramatically redefine the future of transportation. The 3D Perception system of the autonomous car is a critical keystone upon which high level autonomy functions depend. This competition is designed to help advance the state of the art in 3D object detection by focusing research on this topic in the context of autonomous cars, specifically by sharing the full modality of sensor data available to typical autonomous cars, and by providing access to a high fidelity HD map.

Contestants will compete to build the most efficient models that solve ImageNet classification, CIFAR-100 classification, or WikiText-103 language modeling. The competition is focused on efficient inference, and uses a theoretical metric rather than measured inference speed to score entries. We hope that this encourages a mix of submissions that are useful on today’s hardware and that will also guide the direction of new hardware development.

The Animal-AI Olympics translates tasks from animal cognition into tests for AI. We provide a fully configurable 3D environment in which we have built 100 hidden food-retrieval tasks inspired by work in comparative cognition. Participants only know the ten categories we are testing for along with the objects included in the tests and have to submit an agent capable of robust food-retrieval behaviour from pixel inputs alone. Part of the challenge is developing sensible environment configurations as well as mimicking animal-like cognitive abilities.

Predict high resolution traffic flow volume, heading, and speed on a whole city map looking 15 minutes into the future! Kicking off a series of annual competitions, this year's data is based on 100 billion probe points from 3 cities mapped in 5 minute intervals, showing trends across weekdays and seasonal effects. Improved traffic predictions are of great social, environmental, and economic value, while also advancing our general ability to capture the simple implicit rules underlying a complex system and model its future states.

Open-ended learning aims to build learning machines and robots that are able to acquire skills and knowledge in an incremental fashion in a certain environment. This competition addresses autonomous open-ended learning with a focus on simulated robot systems that: (a) acquire a sensorimotor competence that allows them to interact with objects and physical environments; (b) learn in a fully autonomous way, i.e. with no human intervention (e.g., no tasks or reward functions) on the basis of mechanisms such as curiosity, intrinsic motivations, task-free reinforcement learning, self-generated goals, and any other mechanism that might support autonomous learning. The competition challenge will feature two phases: during an initial ""intrinsic phase"" the system will have a certain time to freely explore and learn in an environment containing multiple objects, and then during an ""extrinsic phase"" the quality of the autonomously acquired knowledge will be measured with tasks unknown at design time and during the intrinsic phase.

The third series of a NeurIPS competition to develop human-level versatile locomotion controllers, which is a grand challenge in biomechanics, neuroscience, and robotics. This year, the main task is to develop a controller for a 3D human musculoskeletal simulation model to walk or run following velocity commands. In addition to the solution with the highest reward, we will award solutions that use novel learning techniques and biomechanical knowledge.

The task is to correctly classify images of populations of human cells as exhibiting one of 1,108 different treatments in the dataset RxRx1. RxRx1 consists of 125,514 high-resolution 512x512 6-channel fluorescence microscopy images of cells under these treatments across 4 cell types and 51 different runs of the same experimental design. These images exhibit technical nuisance factors specific to each experiment called batch effects that confound the classification task. The data is split into training and test sets by experiment, so good classifiers will need to separate relevant biological factors from the batch effect factors in order to generalize outside of the training data.

Learning deep representations in which different semantic aspects of data are structurally disentangled is of central importance for advancing artificial intelligence. Despite the clear necessity and benefits of disentangled representations, recent benchmarks on simulated datasets have exposed severe limitations of state-of-the-art approaches. In this NeurlPS challenge we will explicitly address (i) unsupervised learning with unsupervised model selection (ii) role of supervision and (iii) impact and application of disentanglement on real-world images.

A challenge requiring participants to develop sample efficient reinforcment and imitation learning algorithms to solve a complex task in Minecraft, a rich open-world environment featuring sparse-rewards, embodied multi-agent interactions, long-term planning, vision, navigation, and explicit and implicit sub-task hierarchies. The competition features two components: 1) the ObtainDiamond task, a sequential decision making environment requiring an agent to collect a necessary set of requisite items, explore, and mine diamonds only using observations from its first-person perspective; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied agent trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples (4-days worth) from a new Minecraft simulator called MineRLEnv which modifies the Malmo simulator to be synchronous and extremely efficient. Submissions will be evaluated by being trained and then run from scratch by the competition organizers in a fixed cloud-computing in environment to ensure that truly sample-efficient algorithms are developed.

Game of Drones is a multi-drone racing tournament conducted in the high-fidelity simulation environment AirSim. Participants will have the choice of three tiers: Planning only, Perception only, or Full Autonomous Racing. The aim is to combine challenges from adversarial planning and real-time perception and to encourage fusing learning- and model-based approaches.

In the NeurIPS Live Malaria Challenge we are looking for participants to apply machine learning tools to determine novel solutions which could impact malaria policy in Sub Saharan Africa. Specifically, how should combinations of interventions be deployed under budget constraints to impact lives saved and the prevalence of the malaria parasite in a simulated environment.

The third edition of the AI Driving Olympics is designed to probe the state of the art in all areas of autonomous vehicles. This year we will have 3 events: 1) Urban - based on the duckietown platform, 2) Racing - inspired by the AWS DeepRacer platform, and 3) Advanced sensing - using the nuScenes dataset. For each event we provide the necessary tools in the form of simulators, logs, code templates, baseline implementations and low-cost and low barrier of entry access to robotics hardware.